{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e2f8a49e",
   "metadata": {},
   "source": [
    "# **<font size=4 color=#BB3D00 face=微软雅黑>pulstran函数</font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff555dfc",
   "metadata": {},
   "source": [
    "## <font size=3  face=微软雅黑>**※Matlab案例**</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42c14b27",
   "metadata": {},
   "source": [
    "网址：https://ww2.mathworks.cn/help/signal/gs/the-pulstran-function.html?searchHighlight=pulstran&s_tid=srchtitle_pulstran_2  \n",
    "描述：本案例由1个示例构成：   \n",
    "### - <font color=DarkOrChid size=3>示例1：生成由高斯脉冲的多次延迟插值之和组成的脉冲序列</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d340575b",
   "metadata": {},
   "source": [
    "## <font size=3 face=微软雅黑>**※Python案例**</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d16613c8",
   "metadata": {},
   "source": [
    "针对以上案例，采用Python语言实现。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de5c02ba",
   "metadata": {},
   "source": [
    "### - <font color=DarkOrChid size=3>示例1：生成由高斯脉冲的多次延迟插值之和组成的脉冲序列</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f1d6119",
   "metadata": {},
   "source": [
    "该脉冲序列定义为具有 50 kHz 的采样率、10 ms 的脉冲序列长度和 1 kHz 的脉冲重复率。T 指定脉冲序列的采样时刻。D 在第一列中指定每个脉冲重复的延迟，在第二列中指定每个重复的可选衰减。要构造该脉冲序列，将 gauspuls 函数的名称以及附加参数（用于指定带宽为 50% 的 10 kHz 高斯脉冲）传递给 pulstran。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0c2aa6ce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.    0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01 ]\n",
      "0.001\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x252b1252430>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import signal\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "T = np.arange(0, 10e-3 + 1/50e3, 1/50e3)\n",
    "D1 = np.arange(0, 10e-3 + 1/1e3, 1/1e3)\n",
    "D2 = np.array([0.8**0, 0.8**1, 0.8**2, 0.8**3, 0.8**4, 0.8**5, 0.8**6, 0.8**7, 0.8**8, 0.8**9, 0.8**10])\n",
    "print(D1)\n",
    "gauspuls = signal.gausspulse(T-0.002, fc=10e3)\n",
    "print(D1[1])\n",
    "Y = 0\n",
    "for i in range(11):\n",
    "    Y = Y + D2[i]*signal.gausspulse(T-D1[i], fc=10e3)\n",
    "plt.plot(T, Y)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
